Auxiliary information such as canny edges [1]}, [2]}, segmentation maps [3]}, [4]}, L-Edges [5]} and gradients [2]} are shown very useful to inpainting. Specifically, EdgeConnect [1]} utilizes canny edges to help inpainting images with certain structures. E2I [5]} firstly tried to restore images with a two-stage inpainting model enhanced by HED [9]}. Guo et al. [10]} propose a two-stream network for image inpainting, which models the structure constrained texture synthesis and texture-guided structure reconstruction in a coupled manner. SGE-Net [4]} iteratively updates the semantic segmentation maps and the corrupted image.
blackMoreover, RTV is used to filter high-frequency textures for the structural recovery [12]} and the decoupling structure/texture feature learning [13]}. Our previous work [14]} further proposes learning a sketch tensor space, composed of canny edges and lines for inpainting man-made scenes.
blackIn this paper, inspired by the pilot study on auxiliary priors in Sec. REF , L-Edges and lines are employed to model the structural priors.

However, quite differs from [14]}, the transformer is explored to model structural priors in ZITS++. Particularly, preliminary investigations [16]} have shown the excellent capability of transformer in modeling structural relationships for natural image synthesis.
